Remote Photoplethysmography (rPPG) is a contactless noninvasive
method for measuring physiological signals such as
the heart rate (HR) using the light reflected from the facial
tissue. Signal decomposition approaches are used to extract
the heart rate signal from the subtle changes in the skin color.
In this paper, we show that a recently proposed signal decomposition
method, namely nonlinear mode decomposition
(NMD), is quite successful in estimating the heart rate signal
from face videos in the presence of subject motion. Experimental
results on the PureDL dataset show that NMD based
HR estimation gives better results as compared to well-known
methods in the literature, which use Independent Component
Analysis (ICA) for signal decomposition.